This study presents a numerical investigation of a steady, two-dimensional, incompressible turbulent flow with forced convection along a small channel with corrugated walls in a trapezoidal shape. The objective of this study is to evaluate the effect of corrugation geometry on the heat transfer rate and pressure drop through the channel. The constructal design method was applied to the geometry domain with two constraints: the total area of the channel and the area of the trapezoidal corrugation upstream of the channel. Two degrees of freedom are considered: the ratio of the smaller base to the larger base of the upstream trapezoidal corrugation (LA₂/LA₁) and the ratio of the trapezoid’s height to its larger base (H₁/LA₁). All cases were simulated for convective flows with Reynolds and Prandtl numbers of Re_D = 22,000 and Pr = 0.71, respectively. The time-averaged mass, momentum, and energy conservation equations are solved using the Finite Volume Method with the RANS (Reynolds-Averaged Navier-Stokes) turbulence model and the k-ω SST (Shear Stress Transport) turbulence closure model. The results indicate that a specific H₁/LA₁ ratio improves the heat transfer rate by 26.2% compared to the worst case for the same LA₂/LA₁ ratio. Furthermore, larger insertions of trapezoidal corrugations at the bottom of the channel enhance the thermal performance of the heat exchanger, while the insertion of corrugations at the upper part of the channel has a negligible effect on heat transfer performance. From a fluid dynamic perspective, smaller insertions in the fluid flow direction led to lower pressure losses.

Atherosclerosis is a major risk factor for cardiovascular diseases, and its diagnosis is crucial at an early stage. carotid ultrasonography is the current primary diagnostic method for atherosclerosis. However, carotid ultrasonography has problems in the early detection and evaluation of the mechanical properties of the arterial wall. To address these issues, waveform analysis focusing on pulse wave propagation has garnered attention. Despite its potential, few studies have performed pulse wave separation in an environment where pulse waves interfere with each other, as in vivo, and evaluated the reflected waveforms using three-dimensional fluid–structure interaction (FSI) analysis. In this study, pulse wave propagation was reproduced to investigate the relationship between local changes in the mechanical properties of the arterial wall and the reflected waveforms. Using a three-dimensional cylindrical model, coupled FSI analysis was performed with commercial codes by Altair. The results showed that an increase in Young’s modulus amplified the reflected wave amplitudes and elongated the wavelengths. The results also showed trends similar to the theoretical reflection coefficients, particularly for larger changes in Young’s modulus, which closely aligned with the theoretical values. These findings indicate that evaluating reflected waves can lead to estimating the local mechanical properties of the arterial walls.

With the continuous advancement of global information technology, the semiconductor industry has become a cornerstone of the world economy. The complexity and high interdependence of the semiconductor supply chain make its management and optimization a challenging task, particularly in achieving collaborative decision-making across different tiers of suppliers. Traditional research in supply chain management has largely focused on optimizing single-tier suppliers or partial segments of the supply chain, lacking a comprehensive analysis and optimization of multi-tier supplier collaboration. To address this challenge, this study proposes an optimization model based on a three-tier management and collaborative decision-making framework within the semiconductor supply chain. The model captures the intricate collaborative relationships among upstream raw material suppliers, midstream manufacturers, and downstream distributors, aiming to enhance the overall efficiency and responsiveness of the supply chain through coordinated multi-tier decision-making. Existing studies on semiconductor supply chains predominantly emphasize static or localized optimization, often neglecting the dynamic nature of supply chains and lacking systematic research on information sharing and coordination mechanisms. Moreover, these approaches frequently suffer from excessive simplification, inadequate adaptability to dynamic changes, and poor real-world applicability. To overcome these limitations, this paper develops and solves a collaborative optimization model covering three key supply chain tiers and introduces a dynamic framework for adjusting decisions across all tiers of suppliers. The results demonstrate that the proposed model significantly improves overall supply chain coordination, reduces the impact of uncertainties, and enhances both economic performance and market competitiveness.

Sidewalks play a crucial role in enhancing the comfort, safety, and accessibility of pedestrians. However, their design often neglects ergonomic principles, particularly in Makassar City. This study aims to determine ergonomic sidewalk heights using a local anthropometric approach based on knee-to-floor dimensions (LL) and allowance factors. Data were collected from 16 road segments in Makassar through observation, surveys, and statistical analysis. The results revealed that the existing sidewalk heights varied between 16–34 cm, with the highest interval at 28–30 cm (34%). While 96% comply with national standards (10–30 cm), they do not fully address user comfort. By applying allowance factors, the ergonomic sidewalk heights were determined as 11.93 cm (low allowance), 23.85 cm (medium allowance), and 35.78 cm (high allowance). These findings provide essential guidelines for adaptive sidewalk design, improving pedestrian comfort, accessibility, and safety, and are relevant for developing more inclusive urban infrastructure.

The natural fibers are too weak and must be chemically treated to strengthen them. In this article, synthetic and natural fibers were used as reinforcement in epoxy resins with different weight ratios of 2%, 4%, 6%, and 8%. Different epoxy/fiber composites were used to prepare the epoxy/fiber composites. Tensile testing was used to evaluate the mechanical specifications and conditions of composite materials. The results of the study showed the importance of determining the weight ratios of the fibers added to the epoxy resin and the type of distribution. It has been concluded that the addition of fibers to the epoxy matrix improves the tensile behavior and that increasing its amount leads to an increase in the tensile strength in addition to the effect of an increase in its length. The fibers incompletely immersed in the epoxy matrix reduce the tensile behavior. Results showed that using polypropylene with a length of 6 mm will improve the tensile strength from 33.65 MPa to 64.37 MPa, while the other naturally strengthened fibers can enhance the tensile strength in different ratios, such as jute fiber and woven jute fibers. Finally, if the distribution of the fibers in the stress concentration zones is too low, there will be a reduction in the tensile behavior. The results concluded that it would be possible to use both synthetic and natural fibers as secondary fillers for the preparation of composite materials.

This study investigates the mechanical properties of maraging steel MS1 produced through two distinct manufacturing processes: Which include Direct Metal Laser Sintering (DMLS) and Computer Numerical Control (CNC) machining. The goal is to investigate the influence of these methods on mechanical performance and the microstructural integrity of the produced components. The strength, ductility, and fracture behavior of the specimens were evaluated under tensile testing. Results also showed that the DMLS specimen had significantly superior mechanical properties compared to the CNC machined specimen with an ultimate tensile strength of 1145.8 MPa compared to 542.45 MPa. The results indicated that the DMLS specimen withstood higher stress levels, while remaining at lower strain than that of the CNC machined specimen. Which means that the strength and coherence of the structural particles in the DMLS specimen stems from a strong degree of bonding between deposited particles of structured material. Based on fractographic analysis, the DMLS sample showed a more homogenous microstructure due to which metal atom distribution was more coherent and the CNC sample had signs of internal defects due to machining. SOLIDWORKS simulations conducted to validate the results proved to be very close to the experimental results, essentially verifying the reliability of the results. The study concludes that DMLS provides large benefits over conventional CNC machining for the production of high-performance maraging steel components and points to the feasibility of additive manufacturing in advanced engineering applications. Further, we suggest, that future research may include the following investigations to further optimize the mechanical properties of 3D printed maraging steel, including the investigation of additional processing parameters and post-processing treatments.

This research explores integrating a Darrieus Vertical Axis Wind Turbine (VAWT) system to enhance speedboat energy efficiency. With the rising need for sustainable marine energy solutions, harnessing wind power through VAWT technology can reduce fuel consumption and environmental impact. The study focuses on the aerodynamic design, turbine placement, and energy output estimation under varying maritime wind conditions. Vertical-axis turbines efficiently operate without needing wind alignment, making them suitable for marine use. A VAWT was mounted on a 12-meter, four-engine speedboat, powering essential systems like spotlights and navigation. Simulations and field tests assessed power generation, stability, and drag impact. At a speed of 14 knots (7.2 m/s), the turbine produced 11.39 watts, outperforming laboratory results due to uniform wind distribution across the blades. Laboratory experiments confirmed these findings, showing an electrical output of 8.2 watts, sufficient for battery charging. Increasing the wind sweep area could further boost power while maintaining stability. The Darrieus VAWT model demonstrated effective energy harnessing at lower speeds and fuel consumption reduction, highlighting its potential for sustainable maritime applications. Future work will focus on improving material durability and developing automated wind angle adjustments for optimal performance.

Manufacturing processes must use natural language processing (NLP) to provide a user-friendly interface for human-machine interaction. Natural language processing (NLP) presents numerous challenges in the manufacturing environments characterized by Industry 4.0, including language barriers, processing bottlenecks in real-time, and data security challenges. The research develops the Cognitive Language Real-Time Processing Optimization (CLR-TPO) method to address these problems with real-time processing limitations in Industry 4.0 human-machine interactions. The goal is to leverage parallel processing architectures and edge computing to increase communication speed. Using state-of-the-art edge computing and parallel processing architectures, CLR-TPO enhances real-time capabilities to ensure rapid and responsive machine-human interactions. Its adaptive learning abilities enable it to gain more language knowledge and adjust to different languages swiftly. Cognitive computing has the potential to fundamentally change several industrial fields, including intelligent process optimization, supply chain management, quality control, and predictive maintenance. This study explores many applications of CLR-TPO, with an emphasis on how it improves operational efficiency and manufacturing processes. The experimental results show that the proposed CLR-TPO model increases the performance rate of 98.6%, Adaptability Analysis of 97.6%, latency analysis of 14.3%, scalability ratio of 98.9%, and accuracy ratio of 96.7% compared to other existing models.

Shielded metal arc welding achieves copper to stainless steel joints using Cu-based and Fe-based electrodes. ANSYS and SOLIDWORKS models predicted the welding heat distribution and HAZ dimension for both welding electrodes. According to the heat distribution results, deformation and stress distribution for both models were calculated. ANSYS software was used to calculate the HAZ and fusion zone width for both sides and both electrodes; the results showed 1.9 mm on the stainless-steel side, 6.24 mm on the copper side for ECuSi, and 6.7 mm for the stainless-steel side, 7 mm for the copper side in E308 sample. The stress models illustrated higher stress on the stainless steel side for both the welding sample and in fixtures for both sides. The estimated deformation results were 0.40 and 0.48 mm for ECuSi and E308, respectively. Weld zone in Cu-based filled joint consists of uniform structure with Cu solid solution phase. Immiscible Cu and Fe mixture causes weld segregation in Fe base electrode joint. Weld zones containing a combination of phases in the Fe-based filled joints exhibit greater microhardness than the Cu-based joints. Cu-based joint achieves highest tensile value, reaching up to 80% copper tensile strength. Heat treatment causes reduction in dislocation density and increases grain size, resulting heat-affected zone (HAZ) softening on both joints copper side. This softening makes HAZ susceptible to fracture during tensile testing. Every joints fractures in ductile manner and plastic deformation is concentrated on softened copper side. Welding joint filled with Cu displays the most plastic deformation due to the significant displacement of both the welding zone and Cu base metal. This deformation primarily produced by weld high plasticity, which helps reduce stress concentration.

Optimized energy generation and smart distribution in a sustainable manner requires accurate prediction of its consumption. However, the prediction of energy demands of households remains a tedious task due to variations in patterns of energy usage. Mathematical models and artificial intelligence (AI), such as smart energy-efficient designs, strategic planning for smart grids, and Internet of Things (IoT)-enabled smart homes, have recently been considered as solutions to these issues. A major issue encountered in energy consumption prediction systems is their restricted prediction horizons, as well as their dependence on one-step predictions. This study, therefore, suggests an innovative model for the prediction of energy demand that uses a long short-term memory (LSTM) and fractional differential equations (FDLE)-based model. The proposed LSTM-FDLE model was trained to predict the collective active power generated by household devices. LSTM’s memory and sequential learning capabilities were also explored in the proposed model for comprehending the complex temporal dependencies and trends in energy consumption data. The performance of the proposed model was evaluated on real-world household energy usage data and found to achieve good prediction accuracy; the performance of the model was also better than that of some conventional one-step prediction models. Therefore, better energy generation planning, and optimal distribution systems can be achieved by the longer forecasting period provided by the proposed “LSTM” model.

The effects of adding silicon to shape memory alloy (SMA) (Nitinol) were investigated in the current investigation. Most people think that silicon-based SMAs could be a cheaper alternative to NiTi SMA because they have good shape memory properties, good damping capacity, and other useful properties. The alloys were mechanically tested for Vickers microhardness, compression force, shape memory effect (strain recovery), density, and porosity to estimate the Si effect. Powder metallurgy was used to make the alloys. The base alloy (Nitinol) was prepared after sintering treatment at a temperature of 850°C for a period of 6hr. In addition, alloys were prepared from them to find out the effect of adding silicon. These alloys included the base alloy to which silicon was added in proportions of 0%, 3%, 6%, and 9% wt. of Si as their weight ratios. The results showed that increasing the percentage of silicon resulted in improved mechanical properties while 9.0 wt.% Si showed better shape memory properties.

Mobile Ad Hoc Networks (MANETs) plays an important role in various fields; however, this network unavoidably encounters difficulties at the network layer primarily owing to misbehavior or malicious nodes. Among the issues plaguing MANETs, the deliberate and accidental dropping of packets by intermediate nodes emerges as a noteworthy problem requiring attention. The work proposes a novel routing protocol that aims to mitigate the packet dropping problem in a thorough yet efficient manner by selecting only neighbors with proven stability and integrity during route discovery. The protocol devises a neighbor node election tactic reliant on residual status of energy and buffer so that it can compute stable route and avoid those neighbors in route which are having constrained energy and buffer. Additionally, it deploys counter-based authenticated acknowledgments and promiscuous monitoring to enable integrity in route and counter malicious packet drooping. Simulation results show the protocol's efficacy, consistently outperforming existing algorithms in packet delivery and energy efficiency. In conclusion, this work systematically addresses the complexities introduced packet dropping nodes in infrastructure-less networks.

This study develops a data-driven strategy for stunting prevention using the K-Means clustering method, validated through the Elbow Method and Cluster Profiling. The high prevalence of stunting in the research area highlights the need for precise health condition mapping to prioritize effective interventions. Data collected from toddlers in the region were grouped into three distinct clusters, each representing varying levels of risk and requiring tailored prevention strategies. These interventions include contextualized preventive education, optimized based on the specific characteristics and needs of each cluster. The results demonstrate that this method accurately maps health conditions, facilitates targeted interventions, and enhances resource allocation. Additionally, the clustering approach serves as a foundation for creating impactful and relevant health counseling materials to strengthen community education. The study’s main contribution lies in providing a data-driven framework that supports evidence-based public health policy and localized stunting prevention strategies, ensuring adaptability to the unique needs of the research area.

This paper focuses on the study of the use of fused deposition modeling (FDM) in enhancing the process parameters of formed components. Three variables (fill density, layer height, and printing speed) are considered to have a significant and significant effect on the tensile strength of acrylonitrile butadiene styrene specimens. The methodology of this study is based on experiments using the Taguchi strategy. On the other hand, previous studies have mainly focused on analyzing individual process parameters and their effect on the mechanical properties of FDM-manufactured parts. The results of this study, using Taguchi techniques and analysis of variance, show that the largest and most significant effect on the tensile strength of FDM structures was the fill density among the three process parameters. ANOVA results for the average tensile strength with a confidence interval of 66.595%, while ANOVA results for the Young Modulus at a confidence interval of 36.236% and the ANOVA findings for the fractured strength at a confidence interval of 50.228%. A higher F-value indicates that adjusting a process parameter has a greater impact on performance characteristics. In addition, there is a limited effect of the other process variable with a smaller effect, but it was still effective. Finally, valuable insights could be drawn from the results about the correlation between process parameters and mechanical properties of components. The study confirms encouraging results using FDM technology for researchers and future studies in terms of enhancing the structural integrity of the produced components.

Facial expression recognition (FER) remains a challenging task due to variations in facial features, occlusions, and imbalanced datasets, which often lead to misclassification of similar emotions. To address these challenges, this study proposes a hybrid Deep Autoencoder and AdaBoost model, leveraging deep feature extraction and ensemble learning to enhance classification robustness. The experimental evaluation on three benchmark datasets—MMAFEDB, AffectNet, and JAFFE—demonstrates outstanding performance, with the model achieving an AUC and Accuracy of 99.9% and 99.8% on large-scale datasets, while maintaining a strong performance of 94.9% AUC and 91.1% accuracy on smaller datasets. The confusion matrix analysis confirms the model's ability to accurately classify distinct emotions, with minor misclassifications occurring in expressions with overlapping features. These findings highlight the effectiveness of the proposed approach in improving FER accuracy, offering significant benefits for real-world applications such as human-computer interaction, emotion-aware systems, and psychological analysis, while also suggesting future enhancements through domain adaptation and refined feature extraction techniques.

Globally, heart disease is one of the main causes of death. Clinical data analysis is a huge problem when it comes to accurately predicting cardiovascular disease. This work presents a prediction model that makes use of numerous proven classification algorithms and different combinations of information. The goal of this work is to help in the detection of heart disease by employing a hybrid classification system depending on the Binary Harris hawks algorithm (BHHO) and the Logistic regression approach. Also, the Boruta algorithm with random forest is used and compared with the proposed PCA-BHHO algorithm. In this work, the data is first preprocessed, and missing values are filled with mean values. Then, data is scaled using standard scaler, and the proposed hybrid PCA and BHHO are applied to select the best features. RF and logistic regression are employed to classify the patients as heart disease patients or not. For comparison, Boruta is used for feature selection and RF for classification and compared the results with the proposed PCA-BHHO algorithm. Two datasets are utilized to test the proposed model: Statlog and the Cleveland heart disease datasets. The proposed PCA-BHHO algorithm attained an accuracy of 92.59% and 89.33% on the Statlog and the Cleveland datasets, respectively. At the same time, the Boruta-RF algorithm attained an accuracy of 90.14% and 87.64% on the Statlog and Cleveland datasets, respectively.

Currently, weapons storage is conducted manually, employing multiple layers of security systems that are time-consuming. This system exhibits numerous vulnerabilities that jeopardize security in the oversight of weapons storage facilities. This research seeks to develop a weapon storage security system utilizing a soldier identity-based identifier and to document its usage through a web-based interface. This system incorporates the MFRC 522 RFID sensor for identification, integrated with a drop-bolt lock, Arduino Uno Ethernet shield, DC buzzer, relay module, DC jack module, and an emergency module, all connected to a web-based interface. The system undergoes testing through multiple scenarios to evaluate response time and robustness. The test results indicate that this system operates efficiently and enhances response time during the laying off and taking off weapons, as well as data recording in real-time. The system identifies the key owner and exhibits a response time of 10 seconds, whereas the web interface records a response time of under 18.2 seconds during heavy usage.

This study focused on the effects of welding speed and power intensity on distortion and corrosion resistance for alloy steel T-joint weldments. The research was theoretical but also included practical experiments to identify thermodynamic characteristics. The study focuses on the alterations in microstructure and the ability of the weld metal to resist corrosion. Three interconnected modeling operations included structural and thermal evaluations in calculating the microstructure and the deformation of the weld joint. The heat effect zone (HAZ) width in welding speed is 5mm/sec, about 47 mm in the Y direction and 60 mm in the X direction. For 6 mm/sec welding speed, the HAZ was 25 mm in the Y direction and 29 mm in the X direction, and finally, the HAZ width for weldment with 7.5 mm/sec welding speed was 21 mm in the Y direction and 26 mm in the X direction. The highest deformation with 1.08 mm was calculated when welding with lower welding speed and the highest source power. While 0.57 mm deformation was recorded when welding with the highest welding speed and lowest source power intensity. Samples of the weld metal were tested to monitor their weightlessness and corrosion level. The results showed that the size of the HAZ increased with increasing intensities of power. Results reveal that the distortion of weld joint varies inversely with welding velocity and directly relates to power intensity. A microstructural analysis shows that the weld metal has acicular interlocking, polygonal ferrite, and side plates. Acicular ferrite amount influenced weld metal corrosion resistance decreased as power intensities increased. The microstructure of the HAZ is significantly influenced by the intensity of the welding power, which in turn affects the microhardness of the HAZ.

SolidWorks used an optimization approach from the authors to strengthen the structural quality of edge weld designs. The current standard approaches for edge weld analysis evaluation remain insufficiently developed which causes limitations to the functionality of SolidWorks simulation software. A modern weldment analysis procedure stands as the selected research method to predict outcomes across various conditions through weld parameter definition. The SolidWorks simulation model provides an advanced method to construct 3D frame structures with edge-welding through precise weld specifications and effective boundary definition. Standard welding processes together with analytical methods affect outcome precision because weld measurements showed differences from projected values. The design process will split weld component inspections into two separate outcomes which will distinguish between passable dimensions and those that need additional evaluation. The scientific research confirms that all structures require weld modifications whenever external forces surpass either 2000 N or 3000 N during analysis. Results show that maximum stability requires either robust welds or reduced safety procedures or better welding electrodes according to the research data. Engineers leverage this simulated platform as it helps evaluate welded structure loading patterns to improve their live design work. Virtual data processing together with actual application parameters allows engineers to build precise weld designs producing better responses predictions for modern welded frameworks in operational environments.

This study presents a machine learning framework for predicting gold prices by integrating diverse financial indicators, including the NASDAQ-100 index (^NDX), Bitcoin (BTC-USD), and gold futures (GC=F). Using daily high prices from February 2020 to May 2024, the approach incorporates robust preprocessing techniques such as the Box-Cox transformation and Principal Component Analysis (PCA) to address skewness, kurtosis, and multicollinearity, to reduce dimensionality while retaining 96.37% of the variance. A Genetic Algorithm-optimized Multi-Layer Perceptron (MLP) regression model achieved high predictive accuracy with an R² score of 0.98, an RMSE of 23.48 USD, and an MAE of 17.38 USD. Permutation importance analysis highlighted PC1 and PC2 as the most significant predictors, collectively capturing over 96% of the dataset's variance. The results emphasize the effectiveness of integrating stock indices, cryptocurrencies, and traditional financial variables for gold price prediction. This research offers practical applications for investors and policymakers by offering insights into market trends, enhancing decision-making, and bridging traditional and emerging markets in financial forecasting.